Quantum Federated Learning (QFL) recently becomes a promising approach with the
potential to revolutionize Machine Learning (ML). It merges the established strengths of …

Quantum social network analysis (QSNA) is a recent advancement in the interdisciplinary
field of quantum computing and social network analysis. This manuscript comprehensively …

In this study, we explore the innovative domain of Quantum Federated Learning (QFL) as a
framework for training Quantum Machine Learning (QML) models via distributed networks …

Distributed quantum computing, particularly distributed quantum machine learning, has
gained substantial prominence for its capacity to harness the collective power of distributed …

Quantum federated learning (QFL) can facilitate collaborative learning across multiple
clients using quantum machine learning (QML) models, while preserving data privacy …

In the realm of liver transplantation, accurately determining hepatic steatosis levels is crucial.
Recognizing the essential need for improved diagnostic precision, particularly for optimizing …

Federated learning is essential for decentralized, privacy-preserving model training in the
data-driven era. Quantum-enhanced federated learning leverages quantum resources to …

In this article, we explore two types of distributed quantum machine learning (DQML)
methodologies: quantum federated learning and quantum model-parallel learning. We …

The widespread deployment of products powered by machine learning models is raising
concerns around data privacy and information security worldwide. To address this issue …

Drug discovery and development is a highly complex and costly endeavor, typically
requiring over a decade and substantial financial investment to bring a new drug to market …